Investigating consumer dynamics in the adoption of smart home technologies using probabilistic linguistic term sets

Öz

In today’s rapidly advancing technological landscape, smart home technologies are gaining increasing importance, and a widespread adoption process is observed among consumers. Advances in information and communication technologies have facilitated the seamless integration of smart home systems into our daily lives. However, the adoption of these technologies by consumers is influenced by various factors. The systematic analysis of these factors forms a crucial research area that requires an understanding of the complex relationships between technological developments and consumer behaviors. In this context, this study aims to identify the factors influencing the adoption and recommendation of smart home technologies. Taking into account the effects of hesitation in decision-making and evaluation processes using the Probabilistic Linguistic Term Sets method, the study also aims to comprehend the behavioral aspects of consumers. Initially, factors were identified through a literature review, and then data obtained from a survey involving 117 participants were evaluated using the Probabilistic Linguistic Term Sets. As the first step, factor weights were determined, and subsequently, through the use of Pearson correlation, both the relationships between factors and their associations with overall tendencies were analyzed over a total of 212 scenarios. It is believed that the insights gained from this study will contribute to stakeholders in the industry to enhance the understanding of the adoption processes of smart home technologies.

Anahtar Kelimeler

• Smart home technologies
• technology adoption
• strategic management
• fuzzy logic
• correlation analysis

Olasılıklı dil terimi kümeleri yaklaşımı kullanılarak akıllı ev teknolojilerinin benimsenmesinde tüketici dinamiklerinin incelenmesi

Öz

Teknolojik gelişmelerin hız kazandığı günümüzde, akıllı ev teknolojileri giderek daha fazla önem kazanmakta ve tüketiciler arasında yaygın bir benimsenme süreci görülmektedir. Bilgi ve iletişim teknolojilerindeki ilerlemeler, akıllı ev sistemlerinin günlük yaşamımıza daha fazla entegre olmasını sağlamıştır. Ancak, tüketicilerin bu teknolojileri benimsemesi, bir dizi faktör tarafından etkilenmektedir. Bu faktörlerin sistematik analizi, teknolojik gelişmeler ve tüketici davranışları arasındaki karmaşık ilişkilerin anlaşılmasını gerektiren önemli bir araştırma alanını oluşturmaktadır. Bu bağlamda bu çalışma ile akıllı ev teknolojilerinin benimsenmesi ve önerilmesinde etkili olan faktörlerin incelenmesi amaçlanmıştır. Olasılıklı dil terimi kümeleri yöntemi ile karar verme ve değerlendirme süreçlerindeki tereddütlerin etkileri de göz önünde bulundurularak tüketicilerin davranışsal yönlerinin anlaşılması hedeflenmiştir. Bu kapsamda öncelikle literatür taraması ile faktörler belirlenmiş daha sonra 117 katılımcıdan anket çalışması ile elde edilen veriler olasılıklı dil terimi kümeleri yöntemi kullanılarak değerlendirilmiştir. İlk adım olarak faktör ağırlıkları belirlenmiş, daha sonra, Pearson korelasyonu ile hem faktörler arası ilişkiler hem de faktörlerin genel eğilimlerle olan ilişkileri toplam 212 senaryo üzerinden analiz edilmiştir. Elde edilen çıkarımların, sektördeki paydaşlara akıllı ev teknolojilerinin benimsenme süreçlerini daha iyi anlama konusunda katkı sunacağı düşünülmektedir.

Anahtar Kelimeler

• Akıllı ev teknolojileri
• teknoloji adaptasyonu
• stratejik yönetim
• bulanık mantık
• korelasyon analizi

Kaynakça

1. 1. Chang, S., Nam, K., Smart home adoption: the impact of user characteristics and differences in perception of benefits, Buildings, 11 (9), 393, 2021.
2. 2. Ayan, O., Türkay, B., Factors affecting the adoption of smart home systems in the context of technology acceptance model. 2021Akıllı Sistemlerde Yenilikler ve Uygulamaları Konferansı (ASYU), Elazığ-Türkiye, 1-7, IEEE, 6-8 Ekim, 2021.
3. 3. Paetz, A.G., Dütschke, E., Fichtner, W., Smart homes as a means to sustainable energy consumption: A study of consumer perceptions, Journal of consumer policy, 35, 23-41, 2012.
4. 4. Peters, D., Axsen, J., Mallett, A., The role of environmental framing in socio-political acceptance of smart grid: The case of British Columbia, Canada, Renewable and Sustainable Energy Reviews, 82, 1939-1951, 2018.
5. 5. Wilson, C., Hargreaves, T., Hauxwell-Baldwin, R., Benefits and risks of smart home technologies. Energy Policy, 103, 72–83, 2017.
6. 6. Vigderman A., Turner G., Your Complete Smart Home Guide, https://www.security.org/smart-home/ Yayın tarihi: Ocak 30, 2023 Erişim tarihi: Ekim 25, 2023.
7. 7. Howarth, J., 50+ Smart Home Statistics (New 2023 Data), https://explodingtopics.com/blog/smart-home-stats Yayın tarihi: Kasım 14, 2022 Erişim tarihi: Ekim 15, 2023.
8. 8. Statistica, Number of users of smart homes worldwide from 2019 to 2028 https://www.statista.com/forecasts/887613/number-of-smart-homes-in-the-smart-home-market-in-the-world Yayın Tarihi: Kasım 15, 2023; Erişim tarihi: Kasım 15, 2023.

9. 9. We Are Social, Meltwater, Digital 2023 Global Overview Report, https://datareportal.com/reports/digital-2023-global-overview-report Yayın Tarihi: Haziran 1, 2023; Erişim tarihi: Eylül 22, 2023.
10. 10. Parks, E., Privacy and Security: Building Trust in the Connected Home, https://www.parksassociates.com/blogs/home-systems-and-controls/privacy-and-security--building-trust-in-the-connected-home Yayın tarihi: Nisan 14, 2022; Erişim tarihi: Ekim 15, 2023.
11. 11. Hosek, J., Masek, P., Andreev, S., Galinina, O., Ometov, A., Kropfl, F., Koucheryavy, Y., A Symphony of integrated IoT businesses: Closing the gap between availability and adoption. IEEE Communications Magazine, 55 (12), 156-164, 2017.
12. 12. Balta-Ozkan, N., Boteler, B., Amerighi, O., European smart home market development: Public views on technical and economic aspects across the United Kingdom, Germany and Italy, Energy Research & Social Science, 3, 65-77, 2014.
13. 13. Coskun, A., Kaner, G., Bostan, İ., Is smart home a necessity or a fantasy for the mainstream user? A study on users’ expectations of smart household appliances, Int. J. Des., 12, 7–20, 2018.
14. 14. Forni, A., Meulen, R., Gartner Survey Shows Connected Home Solutions Adoption Remains Limited to Early Adopters, https://www.gartner.com/en/newsroom/press-releases/2017-03-06-gartner-survey-shows-connected-home-solutions-adoption-remains-limited-to-early-adopters Yayın tarihi: Mart 6, 2017, Erişim tarihi: Eylül 16, 2023.
15. 15. Mani, Z., Chouk, I., Drivers of consumers’ resistance to smart products, Journal of Marketing Man., 33 (1-2), 76-97, 2017.
16. 16. Kim, Y., Lim, S.E., Choi, J., Estimation of willingness to pay for smart home service by contingent valuation method. Journal of Korean Society for Quality Management, 44 (4), 833-843, 2016.
17. 17. Yang, H., Yu, J., Zo, H., Choi, M., User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33 (2), 256-269, 2016.
18. 18. Park, E., Kim, S., Kim, Y., Kwon, S.J., Smart home services as the next mainstream of the ICT industry: determinants of the adoption of smart home services. Universal Access in the Information Society, 17, 175-190, 2018.
19. 19. Shin, J., Park, Y., Lee, D., Who will be smart home users? An analysis of adoption and diffusion of smart homes. Technological Forecasting and Social Change, 134, 246-253, 2018.
20. 20. Li, B., Yu, J., Research and application on the smart home based on component technologies and Internet of Things. Procedia Engineering, 15, 2087-2092, 2011.
21. 21. Chong, G., Zhihao, L., Yifeng, Y., The research and implement of smart home system based on internet of things. 2011 International Conference on Electronics, Communications and Control (ICECC), 2944-2947, IEEE, Eylül 2011.
22. 22. Soliman, M., Abiodun, T., Hamouda, T., Zhou, J., Lung, C.H., Smart home: Integrating internet of things with web services and cloud computing. IEEE 5th international conference on cloud computing technology and science, 2 (317-320), Aralık, 2013.
23. 23. Feng, S., Setoodeh, P., Haykin, S., Smart home: Cognitive interactive people-centric Internet of Things. IEEE Communications Magazine, 55 (2), 34-39, 2017.
24. 24. Hui, T.K., Sherratt, R.S., Sánchez, D.D., Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies. Future Generation Computer Systems, 76, 358-369, 2017.
25. 25. Mao, X., Li, K., Zhang, Z., Liang, J., Design and implementation of a new smart home control system based on internet of things. 2017 International smart cities conference (ISC2), 1-5, IEEE, Eylül, 2017.
26. 26. Alaa, M., Zaidan, A.A., Zaidan, B.B., Talal, M., Kiah, M.L.M., A review of smart home applications based on Internet of Things. Journal of network and computer applications, 97, 48-65, 2017.
27. 27. Marikyan, D., Papagiannidis, S., Alamanos, E., A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change, 138, 139-154, 2019.
28. 28. Sovacool, B.K., Del Rio, D.D.F., Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies. Renewable and sustainable energy reviews, 120, 109663, 2020.
29. 29. Chopra, S., Al Siyabi, N., Gulliver, S.R., Kyritsis, M., Factors Significantly Impacting Consumer Acceptance of Entertainment, Domestic, and Housekeeping Smart Home IoT Devices., Yayım aşamasında (Version 1: Research Square), https://doi.org/10.21203/rs.3.rs-2068436/v1
30. 30. Green D., Smart Home Market Share, Growth, Statistics, by Application, Production, Revenue & Forecast to 2023-2032, https://www.linkedin.com/pulse/smart-home-market-share-growth-statistics-application-denis-green/ Yayın tarihi: Ağustos, 4, 2023 Erişim tarihi: Ekim, 5, 2023.
31. 31. Shuhaiber, A., Mashal, I., Understanding users’ acceptance of smart homes. Technology in Society, 58, 101110, 2019.
32. 32. Nikou, S., Factors driving the adoption of smart home technology: An empirical assessment. Tele. and Infor., 45, 101283, 2019.
33. 33. Schill, M., Godefroit-Winkel, D., Diallo, M.F., Barbarossa, C., Consumers’ intentions to purchase smart home objects: Do environmental issues matter?. Ecological Economics, 161, 176-185, 2019.
34. 34. Hubert, M., Blut, M., Brock, C., Zhang, R.W., Koch, V., Riedl, R., The influence of acceptance and adoption drivers on smart home usage. European journal of marketing, 53 (6), 1073-1098, 2019.
35. 35. Gultom, R.N., Asvial, M., Analysis of Affecting Technology Adoption Factors for Smart Home Services in Jabodetabek, Indonesia. International Seminar on Intelligent Technology and Its Applications (ISITIA), 326-331, IEEE, Temmuz, 2020.
36. 36. Baudier, P., Ammi, C., Deboeuf-Rouchon, M., Smart home: Highly-educated students' acceptance. Technological Forecasting and Social Change, 153, 119355, 2020.
37. 37. Li, W., Yigitcanlar, T., Erol, I., Liu, A., Motivations, barriers and risks of smart home adoption: From systematic literature review to conceptual framework. Energy Research & Social Science, 80, 102211, 2021.
38. 38. Maswadi, K., Ghani, N.A., Hamid, S., Factors influencing the elderly’s behavioural intention to use smart home technologies in Saudi Arabia. Plos one, 17 (8), e0272525, 2022.
39. 39. Pliatsikas, P., Economides, A.A., Factors influencing intention of Greek consumers to use smart home technology. Applied System Innovation, 5 (1), 26, 2022.
40. 40. Basarir-Ozel, B., Turker, H.B., Nasir, V.A., Identifying the key drivers and barriers of smart home adoption: A thematic analysis from the business perspective. Sustainability, 14 (15), 9053, 2022.
41. 41. Zeng, F., Chen, T.L., A study of the acceptability of smart homes to the future elderly in China. Universal Access in the Information Society, 22 (3), 1007-1025, 2023.
42. 42. Mashal, I., Shuhaiber, A., Al-Khatib, A.W., User acceptance and adoption of smart homes: A decade long systematic literature review. International Journal of Data and Network Science, 7 (2), 533, 2023.
43. 43. Ferreira, L., Oliveira, T., Neves, C., Consumer's intention to use and recommend smart home technologies: The role of environmental awareness. Energy, 263, 125814, 2023.
44. 44. Venkatesh, V., Thong, J.Y., Xu, X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178, 2012.
45. 45. Zadeh, L.A., Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by L A Zadeh (394-432), 1996.
46. 46. Rodriguez, R. M., Martinez, L., Herrera, F., Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on fuzzy systems, 20 (1), 109-119, 2011.
47. 47. Pang, Q., Wang, H., Xu, Z.S., Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences, 369, 128–143, 2016.
48. 48. Lin, M., Xu, Z., Probabilistic linguistic distance measures and their applications in multi-criteria group decision making, Soft computing applications for group decision-making and consensus modeling, Springer International Publishing: Berlin/Heidelberg, Germany, 411-440, 2018.
49. 49. Kobina, A., Liang, D.C., He, X., Probabilistic linguistic power aggregation operators for multicriteria group decision making. Symmetry, 9 (12), 320, 2017.
50. 50. Liu, P.D., You, X.L., Probabilistic linguistic TODIM approach for multiple attribute decisionmaking. Granular Computing, 2 (4), 333–342, 2017.
51. 51. Malik, M.G.A., Bashir, Z., Rashid, T., Ali, J., Probabilistic hesitant intuitionistic linguistic term sets in multi-attribute group decision making. Symmetry, 10 (9), 392, 2018.
52. 52. Liao, H., Mi, X., Xu, Z., A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions. Fuzzy Optimization and Decision Making, 19, 81-134, 2020.
53. 53. Gou, X., Xu, Z., Novel basic operational laws for linguistic terms, hesitant fuzzy linguistic term sets and probabilistic linguistic term sets. Information Sciences, 372, 407-427, 2016.
54. 54. Bai, C. Z., Zhang, R., Qian, L.X., Wu, Y.N. Comparisons of probabilistic linguistic term sets for multi-criteria decision making. Knowledge-Based Systems, 119, 284–291, 2017.
55. 55. Luo, D., Zeng, S., Chen, J., A probabilistic linguistic multiple attribute decision-making based on a new correlation coefficient method and its application in hospital assessment. Mathematics, 8 (3), 340, 2020.
56. 56. Lin, M.W., Xu, Z.S., Probabilistic linguistic distance measures and their applications in multicriteria group decision making. M. Collan, J. Kacprzyk (Eds.), Soft Computing Applications for Group DM and Consensus Modeling, 411–440, 2017.
57. 57. Wang, X.K., Wang, J.Q., Zhang, H.Y., Distance-based multicriteria group decision-makingapproach with probabilistic linguistic term sets. Expert Systems, 36 (2), e12352, 2019.
58. 58. Xian, S.D., Chai, J.H., Yin, Y.B., A visual comparison method and similarity measure for probabilistic linguistic term sets and their applications in multi-criteria decision making. Int. J. Fuzzy Syst., 21, 1154–1169, 2019.
59. 59. Lin, H.B., Jiang, L., Xu, Z.S., Entropy measures of probabilistic linguistic term sets. Int.l J. Comput. Intell. Syst., 11, 45–87, 2018.
60. 60. Mao, X.B., Wu, M., Shang, N., The multi-attribute group decision making model based on probabilistic linguistic correlation coeffcient. J. Jiangxi Norm. Univ. (Nat. Sci. Ed.), 42, 267–274, 2018.
61. 61. Wu, X.L., Liao, H.C., Xu, Z.S., Hafezalkotob, A. Probabilistic linguistic MULTIMOORA: A multicriteria decision making method based on the probabilistic linguistic expectation, IEEE transactions on Fuzzy Systems, 26 (6), 3688-3702, 2018.
62. 62. Liu, P.D., Li, Y. The PROMTHEE II method based on probabilistic linguistic information and their application to decision making. Information, 29, 303–320, 2018.
63. 63. Zhang, Y.X., Xu, Z.S., Liao, H.C., Water security evaluation based on the TODIM method with probabilistic linguistic term sets. Soft Computing, 23, 6215–6230, 2019.
64. 64. Liu, P.D., Li, Y., A Novel Decision-Making Method Based on Prob. Linguistic Information. Cogn. Computing, 11, 735–747, 2019.
65. 65. Wu, X., Liao, H., An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision-making, Information Fusion, 43, 13-26, 2018.
66. 66. Lu, J.P., Wei, C., Wu, J., Wei, G.W., TOPSIS method for probabilistic linguistic MAGDM with entropy weight and its application to supplier selection of new agricultural machinery products. Entropy, 21, 953, 2019.
67. 67. Kalender, Z.T., Tozan, H., Vayvay, O., Prioritization of medical errors in patient safety management: Framework using interval-valued intuitionistic fuzzy sets, Healthcare, 8 (3), 265, 2020.
68. 68. Yi, Z. Decision-making based on probabilistic linguistic term sets without loss of information. Complex & Intelligent Systems, 8 (3), 2435-2449, 2022.
69. 69. Han, X., Zhang, C., Zhan, J., A three-way decision method under probabilistic linguistic term sets and its application to Air Quality Index. Information Sciences, 617, 254-276, 2022.
70. 70. Qin, Y., Hashim, S.R.M., Sulaiman, J., Probabilistic linguistic multi-attribute decision-making approach based upon novel GMSM operators. AIMS Mathematics, 8 (5), 11727-11751, 2023.
71. 71. DeVellis, R. F., Scale development: Theory and applications. 2. Baskı, Sage publications, California, A.B.D., 2003.
72. 72. Cohen, J., Cohen, P., West, S.G., Aiken, L.S. Applied multiple regression/correlation analysis for the behavioral sciences. 3. Baskı, Routledge, New York, A.B.D., 2013.
73. 73. Şahin, Z., Dijital Dönüşümün, Örgütsel İnovasyona Etkilerinin Analizi: İstanbul Beylikdüzü Organize Sanayi Bölgesi Örneği. İşletme Araştırmaları Dergisi, 15 (1), 486-499, 2023.
74. 74. Ratner, B., The correlation coefficient: Its values range between+ 1/− 1, or do they?, Journal of targeting, measurement and analysis for marketing, 17 (2), 139-142, 2009.
75. 75. Obilor, E.I., Amadi, E.C., Test for significance of Pearson’s correlation coefficient. International Journal of Innovative Mathematics, Statistics & Energy Policies, 6 (1), 11-23, 2018.
76. 76. Schober, P., Boer, C., Schwarte, L.A. Correlation coefficients: appropriate use and interpretation. Anesthesia & analgesia, 126 (5), 1763-1768, 2018.
77. 77. Şenel, B., Şenel, M., An Analysis of Technology Acceptance in Turkey using Fuzzy Logic and Structural Equation Modelling. İşletme Araştırmaları Dergisi, 3 (4), 34-48, 2011.
78. 78. İnce H., Imamoglu S., Keskin H. Comparing Self Organizing Maps with K-Means Clsutering: An Application to Customer Profiling, Journal of the Faculty of Engineering and Architecture of Gazi University, 28 (4), 723-732, 2013.
79. 79. Boyaci A.C., Solmaz M.B., Kabak M., A model proposal for occupational health and safety risk assessment based on multi-criteria hesitant fuzzy linguistic term sets: An application in plastics industry, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (2), 1041-1053, 2021.
80. 80. Selamoğulları U., Elma O., A smart transformer application for voltage-controlled home energy management system, Journal of the Faculty of Engineering and Architecture of Gazi University, 33 (4), 1543-1556, 2018.